Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4501-2023
© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Aug 2023
Development and technical paper |
| 10 Aug 2023
| 10 Aug 2023
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
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Cited articles
Agarwal, N., Kondrashov, D., Dueben, P., Ryzhov, E., and Berloff, P.: A
Comparison of Data-Driven Approaches to Build Low-Dimensional Ocean Models,
J. Adv. Model. Earth Sy., 13, e2021MS002537, https://doi.org/10.1029/2021MS002537, 2021. a
Arcomano, T., Szunyogh, I., Wikner, A., Pathak, J., Hunt, B. R., and Ott, E.: A
Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a
Physics-Based Numerical Model, J. Adv. Model. Earth Sy.,
14, e2021MS002712, https://doi.org/10.1029/2021MS002712, 2022. a, b
Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein generative adversarial
networks, in: International conference on machine learning,
PMLR, 214–223, https://doi.org/10.48550/arXiv.1701.07875, 2017. a
Arnold, H. M., Moroz, I. M., and Palmer, T. N.: Stochastic parametrizations and
model uncertainty in the Lorenz’96 system, Philosophical Transactions of
the Royal Society A: Mathematical, Phys. Eng. Sci., 371,
20110479, https://doi.org/10.1098/rsta.2011.0479, 2013. a, b, c, d
Bahdanau, D., Cho, K., and Bengio, Y.: Neural machine translation by jointly
learning to align and translate, arXiv [preprint],
https://doi.org/10.48550/arXiv.1409.0473, 2014. a
